00:34:11 · CJ Gustafson
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00:35:54 · Brian Neider
[00:35:54] S0: I think that oftentimes we would look at it as two separate line items. So you're gonna look at what is the multiple of recurring revenue on a software business or an AI business? What does that look like? And then you're gonna look at the services business, and you're gonna say, are they losing money here? Is it sort of breakeven? And if they're losing money, it's almost like that's part of your your sales and marketing expense. Right? It just really depends. So as a shorthand, you're gonna look at the overall revenue multiples. And if it's a situation where the services revenue is so remarkably high relative to the software revenue, you have to take both into account. If the services revenue is 10% of the overall software revenue in terms of size, we get your shorthand, and then you could sort of dig in more granularly down the line. At the end of the day, there's usually some type of services that are necessary. We've actually done pretty well, and there's a little bit of aside from the question, but it's worth mentioning, which is a lot of software companies try to offload their services because they're worried that people are gonna look at it and say, gosh, you've got this sort of weight around your ankle in the form of a services business. You shouldn't have that. So companies like Accenture and Deloitte and a lot of the, you know, IT outsourcers in India have built enormous consulting practices around, like, implementation of software, ongoing training and integrations and change management, you know, and the like. In the past, we've actually looked at a lot of services businesses who are helping to create the ecosystems for the really rapidly growing software vendors, and that's actually a theme that we that we really like. And so years ago, Salesforce and Workday, like, really, really take off. We're like, gosh. You know, even the big consulting firms that are trying to help with this can't keep up. There must be a couple of businesses that are services oriented companies that are drafting off of the growth in these ecosystems. And so we ended up making investment into a company called Aperio, which we eventually sold to Wipro. And Aperio sort of helped a partner of Workday, a partner of Salesforce, do the implementations and help do the training and, you know, everything that was necessary to get the client...
00:38:04 · Brian Neider
The Workday and Salesforce clients up and running. And then we sort of took that theme, and we invested in a company called Hakkoda, which provided services within Snowflake ecosystem. And that business was acquired by IBM. Same sort of thought process, which was the big consulting firms are gonna have a hard time keeping up with this. There's probably some very specific newer age businesses that can do this. And most recently, we made our third play in a similar thematic element, which is business called Advancing Analytics, which provides services for the Databricks ecosystem, same type of thing. You know? There's just so much work to do within the Databricks ecosystem, and there's that. So we like investing in services businesses if they're tethered to the right tailwind, and they could be very, very attractive. And usually, the bigger consulting firms are excited to make acquisitions there. And as it relates to the software companies or AI companies themselves, usually they're willing to tolerate and do some level of, you know, services engagement, but more often than not, they wanna they wanna push it off because it's not really their exact business.
00:39:10 · CJ Gustafson, Brian Neider
[00:39:10] S1: Oh, totally. So one of our friends of the show, Rillet, they have an agentic ERP, and I was at their conference doing a panel, and I met two different people from two different services companies who said, oh, no. We specialize in implementing Rillet now. So I'm like, oh, wow. There's a cottage industry for this. I can see how that would make you really successful if the platform is really successful and vice versa. [00:39:31] S0: A 100%. And I think that it's a great way to be investing. It's a great way to be understanding. And with every great software platform, depending upon the complexity, of course, there's usually some type of services engagement, services ecosystem that ends up falling behind it, and there's a lot of precedent for really exciting outcomes in that regard. [00:39:53] S1: So, Brian, you break software into three buckets, horizontal, vertical, and infrastructure. Can you just explain the difference between those three and why AI affects each one so differently?
00:40:05 · Brian Neider
[00:40:05] S0: Part of this will be, you know, purely opinion. Part of it'll be a little bit more factual to answer that question. But I think broadly, when you think about software, if if you were to talk to a a software analyst at an investment bank or something like that and how they might organize their coverage, the world would largely be broken up into application software on one side, infrastructure software on the other side. And the way that I learned twenty years ago to think about this, and I don't think that that much has changed, On the application side, you could think of something as an application software business if it's selling or if its users, excuse me, are are like business oriented users. And so think that that non techie people are the ones primarily using it. Infrastructure software conversely, usually you have... You will have techie people typically using it. That's how you start. Now each one of those areas would break down a little bit further. So let's take application software to start. Application software typically, once you break that down one level deeper, you'll have horizontal applications and you'll have vertical applications. A vertical application would be something that's very specific to a singular end market. So we have like a business that does software for the cardiac market. Right? So patients that have defibrillators or pacemakers, that would be a a vertical market within that... Within electrophysiology. You know, there's businesses that are public, like AppFolio, which has vertical software for the real estate market. You have companies like Procore, which are public, that is vertical software within the construction market. And so that's vertical. Horizontal is more software typically for a business function across any type of accounting software or HR software or marketing software. In those cases, it's typically not all that important what the end company does. If they're doing some type of marketing function, they'll probably want some type of marketing software. If they're doing some type of accounting software, they're probably gonna want... Accounting function, they're probably gonna want some type of accounting software. And so on that side, the application side of the house, you kind of have horizontal and vertical.
00:42:15 · Brian Neider
Now the question as it relates to AI and and how AI might affect it, and this is more of an opinion in my case than than than fact, whereas the rest is sort of, I think, most people would agree on. When I think about how AI would affect it, I think that in vertical, it's harder. Vertical software is extremely mission critical typically. There's a lot of nuance and know how that only can come through significant conversations with customers, understanding of what they're trying to do, what the outcomes they're trying to get are, and specifically how they go about doing their work. And so vertical software ends up being quite particular, quite tailored to that industry. And I think that while AI will absolutely have effects within vertical software, it's slightly more difficult because the nuance and know how in those products and how they serve those niche industries are so specific that it's it's pretty hard to replicate. On the other side with horizontal software, I think that that oftentimes is looked on, and and and I would agree, as a little bit more susceptible for AI risk. And the reason is oftentimes those software platforms are more one size fits all. So as opposed to having, like, really deep understanding and workflows about how a construction firm might need to build out their project, it's more catch all across an entire functional ecosystem. And I think that those workflows are a little bit easier to sort of ascertain, understand, and in many cases, that's that's where you might have issue. So when I think about on the application side where there's issues versus where there's not, probably more issues on horizontal, less issues for AI on on vertical. There's exceptions to every rule. If you move over to the infrastructure side, and these are more, again, like for software for techie users, there's different buckets within infrastructure, but you might think about network management tools, you might think about cybersecurity tools, you might think about developer tools. And within those worlds, I think that tools around network management, observability, performance monitoring, probably in an AI world doing a little bit better actually. There's so many more devices. There's so many more agents. There's so much more data flowing through the systems.